Overview

Dataset statistics

Number of variables22
Number of observations105
Missing cells29
Missing cells (%)1.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory18.2 KiB
Average record size in memory177.2 B

Variable types

Categorical4
Numeric18

Alerts

CROP has constant value "Rainfed maize"Constant
STATIONNAME has a high cardinality: 105 distinct valuesHigh cardinality
LONGITUDE is highly overall correlated with ELEVATION_METER and 2 other fieldsHigh correlation
LATITUDE is highly overall correlated with COUNTRYHigh correlation
ELEVATION_METER is highly overall correlated with LONGITUDE and 2 other fieldsHigh correlation
YA is highly overall correlated with ELEVATION_METER and 6 other fieldsHigh correlation
YW is highly overall correlated with YA and 7 other fieldsHigh correlation
YW-YA is highly overall correlated with YW and 7 other fieldsHigh correlation
YP is highly overall correlated with YW and 5 other fieldsHigh correlation
YP-YA is highly overall correlated with YW-YA and 1 other fieldsHigh correlation
WPP is highly overall correlated with YW and 5 other fieldsHigh correlation
WPA is highly overall correlated with YAHigh correlation
YW_CV_TEMPORAL is highly overall correlated with YA and 6 other fieldsHigh correlation
CLIMATEZONE is highly overall correlated with LONGITUDE and 1 other fieldsHigh correlation
MIN_N_INPUT_TARGET_30_PERC is highly overall correlated with YA and 7 other fieldsHigh correlation
MIN_N_INPUT_TARGET_50_PERC is highly overall correlated with YA and 7 other fieldsHigh correlation
MIN_N_INPUT_TARGET_80_PERC is highly overall correlated with YA and 7 other fieldsHigh correlation
COUNTRY is highly overall correlated with LONGITUDE and 2 other fieldsHigh correlation
CROPPING_INTENSITY is highly overall correlated with COUNTRYHigh correlation
CROPPING_INTENSITY is highly imbalanced (60.0%)Imbalance
ELEVATION_METER has 2 (1.9%) missing valuesMissing
YA_CV_TEMPORAL has 27 (25.7%) missing valuesMissing
STATIONNAME is uniformly distributedUniform
STATIONNAME has unique valuesUnique
LONGITUDE has unique valuesUnique
YW has unique valuesUnique
YW-YA has unique valuesUnique
YP-YA has unique valuesUnique
WPP has unique valuesUnique
WPA has unique valuesUnique
YW_CV_TEMPORAL has unique valuesUnique
YP_CV_TEMPORAL has unique valuesUnique
AREA_IN_CLIMATEZONE_HA has unique valuesUnique
MIN_N_INPUT_TARGET_30_PERC has unique valuesUnique
MIN_N_INPUT_TARGET_50_PERC has unique valuesUnique
MIN_N_INPUT_TARGET_80_PERC has unique valuesUnique

Reproduction

Analysis started2023-02-26 04:16:53.527235
Analysis finished2023-02-26 04:18:28.709649
Duration1 minute and 35.18 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

STATIONNAME
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct105
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size968.0 B
Bobo-Dioulasso
 
1
Benin
 
1
tan_rfmz5
 
1
tan_rfmz4
 
1
tan_rfmz3
 
1
Other values (100)
100 

Length

Max length17
Median length13
Mean length6.9428571
Min length2

Characters and Unicode

Total characters729
Distinct characters56
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique105 ?
Unique (%)100.0%

Sample

1st rowBobo-Dioulasso
2nd rowBogandé
3rd rowBoromo
4th rowDédougou
5th rowFada Ngourma

Common Values

ValueCountFrequency (%)
Bobo-Dioulasso 1
 
1.0%
Benin 1
 
1.0%
tan_rfmz5 1
 
1.0%
tan_rfmz4 1
 
1.0%
tan_rfmz3 1
 
1.0%
tan_rfmz1 1
 
1.0%
Sumbawanga 1
 
1.0%
Solesia 1
 
1.0%
Singida 1
 
1.0%
Shinyanga 1
 
1.0%
Other values (95) 95
90.5%

Length

2023-02-26T07:18:28.892158image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
dag 2
 
1.8%
bobo-dioulasso 1
 
0.9%
shire 1
 
0.9%
boromo 1
 
0.9%
dédougou 1
 
0.9%
ngourma 1
 
0.9%
fada 1
 
0.9%
gaoua 1
 
0.9%
ouahigouya 1
 
0.9%
adet 1
 
0.9%
Other values (100) 100
90.1%

Most occurring characters

ValueCountFrequency (%)
a 103
 
14.1%
i 48
 
6.6%
o 46
 
6.3%
r 45
 
6.2%
u 37
 
5.1%
m 35
 
4.8%
e 34
 
4.7%
n 34
 
4.7%
g 25
 
3.4%
s 24
 
3.3%
Other values (46) 298
40.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 580
79.6%
Uppercase Letter 110
 
15.1%
Connector Punctuation 15
 
2.1%
Decimal Number 15
 
2.1%
Space Separator 6
 
0.8%
Dash Punctuation 3
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 103
17.8%
i 48
 
8.3%
o 46
 
7.9%
r 45
 
7.8%
u 37
 
6.4%
m 35
 
6.0%
e 34
 
5.9%
n 34
 
5.9%
g 25
 
4.3%
s 24
 
4.1%
Other values (15) 149
25.7%
Uppercase Letter
ValueCountFrequency (%)
K 19
17.3%
S 13
11.8%
A 13
11.8%
B 12
10.9%
M 10
9.1%
D 9
8.2%
O 5
 
4.5%
N 4
 
3.6%
G 4
 
3.6%
L 3
 
2.7%
Other values (10) 18
16.4%
Decimal Number
ValueCountFrequency (%)
1 4
26.7%
3 3
20.0%
2 2
13.3%
5 2
13.3%
4 1
 
6.7%
9 1
 
6.7%
8 1
 
6.7%
6 1
 
6.7%
Connector Punctuation
ValueCountFrequency (%)
_ 15
100.0%
Space Separator
ValueCountFrequency (%)
6
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 690
94.7%
Common 39
 
5.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 103
 
14.9%
i 48
 
7.0%
o 46
 
6.7%
r 45
 
6.5%
u 37
 
5.4%
m 35
 
5.1%
e 34
 
4.9%
n 34
 
4.9%
g 25
 
3.6%
s 24
 
3.5%
Other values (35) 259
37.5%
Common
ValueCountFrequency (%)
_ 15
38.5%
6
 
15.4%
1 4
 
10.3%
- 3
 
7.7%
3 3
 
7.7%
2 2
 
5.1%
5 2
 
5.1%
4 1
 
2.6%
9 1
 
2.6%
8 1
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 727
99.7%
None 2
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 103
 
14.2%
i 48
 
6.6%
o 46
 
6.3%
r 45
 
6.2%
u 37
 
5.1%
m 35
 
4.8%
e 34
 
4.7%
n 34
 
4.7%
g 25
 
3.4%
s 24
 
3.3%
Other values (45) 296
40.7%
None
ValueCountFrequency (%)
é 2
100.0%

LONGITUDE
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct105
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.887622
Minimum-11.4
Maximum42.1
Zeros0
Zeros (%)0.0%
Negative19
Negative (%)18.1%
Memory size968.0 B
2023-02-26T07:18:29.093619image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-11.4
5-th percentile-4.7274
Q16.733
median31.677681
Q335.3
95-th percentile38.17
Maximum42.1
Range53.5
Interquartile range (IQR)28.567

Descriptive statistics

Standard deviation16.225326
Coefficient of variation (CV)0.70891271
Kurtosis-1.1873488
Mean22.887622
Median Absolute Deviation (MAD)5.4435186
Skewness-0.71525308
Sum2403.2003
Variance263.26121
MonotonicityNot monotonic
2023-02-26T07:18:29.305095image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-4.317 1
 
1.0%
5.57 1
 
1.0%
32.28683039 1
 
1.0%
30.87034241 1
 
1.0%
31.5156132 1
 
1.0%
33.62356705 1
 
1.0%
31.57 1
 
1.0%
31.95 1
 
1.0%
34.733 1
 
1.0%
33.4333 1
 
1.0%
Other values (95) 95
90.5%
ValueCountFrequency (%)
-11.4 1
1.0%
-7.95 1
1.0%
-6.15 1
1.0%
-5.68 1
1.0%
-5.47 1
1.0%
-4.83 1
1.0%
-4.317 1
1.0%
-3.483 1
1.0%
-3.183 1
1.0%
-2.933 1
1.0%
ValueCountFrequency (%)
42.1 1
1.0%
42.03 1
1.0%
39.33 1
1.0%
39.15 1
1.0%
38.334 1
1.0%
38.22 1
1.0%
37.97 1
1.0%
37.835 1
1.0%
37.78 1
1.0%
37.736 1
1.0%

LATITUDE
Real number (ℝ)

Distinct103
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4268764
Minimum-17.735
Maximum14.48
Zeros0
Zeros (%)0.0%
Negative33
Negative (%)31.4%
Memory size968.0 B
2023-02-26T07:18:29.498536image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-17.735
5-th percentile-13.217389
Q1-1.241
median6.2
Q310.067
95-th percentile13.0084
Maximum14.48
Range32.215
Interquartile range (IQR)11.308

Descriptive statistics

Standard deviation8.256232
Coefficient of variation (CV)2.4092587
Kurtosis-0.25353545
Mean3.4268764
Median Absolute Deviation (MAD)5.2267
Skewness-0.8206899
Sum359.82203
Variance68.165367
MonotonicityNot monotonic
2023-02-26T07:18:29.681085image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.2 2
 
1.9%
4.704867512 2
 
1.9%
11.167 1
 
1.0%
-3.4294 1
 
1.0%
-3.651871512 1
 
1.0%
-4.735190291 1
 
1.0%
-2.629463008 1
 
1.0%
-2.689588813 1
 
1.0%
-8 1
 
1.0%
-8.77 1
 
1.0%
Other values (93) 93
88.6%
ValueCountFrequency (%)
-17.735 1
1.0%
-16.81166667 1
1.0%
-15.2545 1
1.0%
-15.0667 1
1.0%
-14.44388889 1
1.0%
-13.56222222 1
1.0%
-11.83805556 1
1.0%
-11.56912128 1
1.0%
-11.1 1
1.0%
-10.65747293 1
1.0%
ValueCountFrequency (%)
14.48 1
1.0%
14.1 1
1.0%
13.567 1
1.0%
13.4 1
1.0%
13.33 1
1.0%
13.017 1
1.0%
12.974 1
1.0%
12.53 1
1.0%
12.467 1
1.0%
12.38 1
1.0%

ELEVATION_METER
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct94
Distinct (%)91.3%
Missing2
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean1079.5437
Minimum20
Maximum3525
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size968.0 B
2023-02-26T07:18:29.901496image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile87.1
Q1346.5
median1120
Q31562.5
95-th percentile2119
Maximum3525
Range3505
Interquartile range (IQR)1216

Descriptive statistics

Standard deviation716.10194
Coefficient of variation (CV)0.66333762
Kurtosis-0.054866761
Mean1079.5437
Median Absolute Deviation (MAD)678
Skewness0.40968776
Sum111193
Variance512802
MonotonicityNot monotonic
2023-02-26T07:18:30.093948image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1105 2
 
1.9%
986 2
 
1.9%
1171 2
 
1.9%
1880 2
 
1.9%
1402 2
 
1.9%
1840 2
 
1.9%
172 2
 
1.9%
243 2
 
1.9%
1384 2
 
1.9%
44 1
 
1.0%
Other values (84) 84
80.0%
(Missing) 2
 
1.9%
ValueCountFrequency (%)
20 1
1.0%
27 1
1.0%
44 1
1.0%
47 1
1.0%
79 1
1.0%
87 1
1.0%
88 1
1.0%
111 1
1.0%
143 1
1.0%
172 2
1.9%
ValueCountFrequency (%)
3525 1
1.0%
2550 1
1.0%
2470 1
1.0%
2240 1
1.0%
2200 1
1.0%
2120 1
1.0%
2110 1
1.0%
2100 1
1.0%
2093 1
1.0%
2060 1
1.0%

COUNTRY
Categorical

Distinct9
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Memory size968.0 B
Ethiopia
22 
Nigeria
16 
Tanzania
15 
Uganda
13 
Zambia
11 
Other values (4)
28 

Length

Max length12
Median length8
Mean length7
Min length4

Characters and Unicode

Total characters735
Distinct characters30
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBurkina Faso
2nd rowBurkina Faso
3rd rowBurkina Faso
4th rowBurkina Faso
5th rowBurkina Faso

Common Values

ValueCountFrequency (%)
Ethiopia 22
21.0%
Nigeria 16
15.2%
Tanzania 15
14.3%
Uganda 13
12.4%
Zambia 11
10.5%
Kenya 8
 
7.6%
Burkina Faso 7
 
6.7%
Ghana 7
 
6.7%
Mali 6
 
5.7%

Length

2023-02-26T07:18:30.304421image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-26T07:18:30.526823image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
ethiopia 22
19.6%
nigeria 16
14.3%
tanzania 15
13.4%
uganda 13
11.6%
zambia 11
9.8%
kenya 8
 
7.1%
burkina 7
 
6.2%
faso 7
 
6.2%
ghana 7
 
6.2%
mali 6
 
5.4%

Most occurring characters

ValueCountFrequency (%)
a 173
23.5%
i 115
15.6%
n 65
 
8.8%
h 29
 
3.9%
o 29
 
3.9%
g 29
 
3.9%
e 24
 
3.3%
r 23
 
3.1%
E 22
 
3.0%
t 22
 
3.0%
Other values (20) 204
27.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 616
83.8%
Uppercase Letter 112
 
15.2%
Space Separator 7
 
1.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 173
28.1%
i 115
18.7%
n 65
 
10.6%
h 29
 
4.7%
o 29
 
4.7%
g 29
 
4.7%
e 24
 
3.9%
r 23
 
3.7%
t 22
 
3.6%
p 22
 
3.6%
Other values (9) 85
13.8%
Uppercase Letter
ValueCountFrequency (%)
E 22
19.6%
N 16
14.3%
T 15
13.4%
U 13
11.6%
Z 11
9.8%
K 8
 
7.1%
B 7
 
6.2%
F 7
 
6.2%
G 7
 
6.2%
M 6
 
5.4%
Space Separator
ValueCountFrequency (%)
7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 728
99.0%
Common 7
 
1.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 173
23.8%
i 115
15.8%
n 65
 
8.9%
h 29
 
4.0%
o 29
 
4.0%
g 29
 
4.0%
e 24
 
3.3%
r 23
 
3.2%
E 22
 
3.0%
t 22
 
3.0%
Other values (19) 197
27.1%
Common
ValueCountFrequency (%)
7
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 735
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 173
23.5%
i 115
15.6%
n 65
 
8.8%
h 29
 
3.9%
o 29
 
3.9%
g 29
 
3.9%
e 24
 
3.3%
r 23
 
3.1%
E 22
 
3.0%
t 22
 
3.0%
Other values (20) 204
27.8%

CROP
Categorical

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size968.0 B
Rainfed maize
105 

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters1365
Distinct characters10
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRainfed maize
2nd rowRainfed maize
3rd rowRainfed maize
4th rowRainfed maize
5th rowRainfed maize

Common Values

ValueCountFrequency (%)
Rainfed maize 105
100.0%

Length

2023-02-26T07:18:30.705353image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-26T07:18:31.012488image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
rainfed 105
50.0%
maize 105
50.0%

Most occurring characters

ValueCountFrequency (%)
a 210
15.4%
i 210
15.4%
e 210
15.4%
R 105
7.7%
n 105
7.7%
f 105
7.7%
d 105
7.7%
105
7.7%
m 105
7.7%
z 105
7.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1155
84.6%
Uppercase Letter 105
 
7.7%
Space Separator 105
 
7.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 210
18.2%
i 210
18.2%
e 210
18.2%
n 105
9.1%
f 105
9.1%
d 105
9.1%
m 105
9.1%
z 105
9.1%
Uppercase Letter
ValueCountFrequency (%)
R 105
100.0%
Space Separator
ValueCountFrequency (%)
105
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1260
92.3%
Common 105
 
7.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 210
16.7%
i 210
16.7%
e 210
16.7%
R 105
8.3%
n 105
8.3%
f 105
8.3%
d 105
8.3%
m 105
8.3%
z 105
8.3%
Common
ValueCountFrequency (%)
105
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1365
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 210
15.4%
i 210
15.4%
e 210
15.4%
R 105
7.7%
n 105
7.7%
f 105
7.7%
d 105
7.7%
105
7.7%
m 105
7.7%
z 105
7.7%

YA
Real number (ℝ)

Distinct101
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9113315
Minimum0.5
Maximum3.98451
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size968.0 B
2023-02-26T07:18:31.165080image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile0.84826253
Q11.3221489
median1.7833273
Q32.524
95-th percentile3.2795665
Maximum3.98451
Range3.48451
Interquartile range (IQR)1.2018511

Descriptive statistics

Standard deviation0.77564987
Coefficient of variation (CV)0.40581651
Kurtosis-0.50461996
Mean1.9113315
Median Absolute Deviation (MAD)0.53032734
Skewness0.53828079
Sum200.6898
Variance0.60163272
MonotonicityNot monotonic
2023-02-26T07:18:31.391473image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.249422134 2
 
1.9%
1.4 2
 
1.9%
0.8 2
 
1.9%
3.279566523 2
 
1.9%
1.88129474 1
 
1.0%
1.253 1
 
1.0%
1.856 1
 
1.0%
1.815194481 1
 
1.0%
1.421805552 1
 
1.0%
0.8309121993 1
 
1.0%
Other values (91) 91
86.7%
ValueCountFrequency (%)
0.5 1
1.0%
0.7938563338 1
1.0%
0.8 2
1.9%
0.8309121993 1
1.0%
0.8336109257 1
1.0%
0.9068689462 1
1.0%
0.9205659657 1
1.0%
0.9555824213 1
1.0%
0.9888551282 1
1.0%
0.9987451787 1
1.0%
ValueCountFrequency (%)
3.984509987 1
1.0%
3.72 1
1.0%
3.51675 1
1.0%
3.345 1
1.0%
3.3061 1
1.0%
3.279566523 2
1.9%
3.249422134 2
1.9%
3.1853 1
1.0%
3.027833333 1
1.0%
3.0196 1
1.0%

YW
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct105
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.352703
Minimum2.2415103
Maximum19.725941
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size968.0 B
2023-02-26T07:18:31.691670image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2.2415103
5-th percentile3.9025117
Q16.8943721
median10.193182
Q313.071842
95-th percentile17.769474
Maximum19.725941
Range17.48443
Interquartile range (IQR)6.17747

Descriptive statistics

Standard deviation4.249729
Coefficient of variation (CV)0.41049463
Kurtosis-0.79725013
Mean10.352703
Median Absolute Deviation (MAD)3.205134
Skewness0.14056561
Sum1087.0338
Variance18.060197
MonotonicityNot monotonic
2023-02-26T07:18:31.910088image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.803804065 1
 
1.0%
9.321842105 1
 
1.0%
7.47225 1
 
1.0%
12.10540404 1
 
1.0%
4.83295 1
 
1.0%
3.01595 1
 
1.0%
15.82625 1
 
1.0%
13.01885 1
 
1.0%
6.08 1
 
1.0%
4.170453361 1
 
1.0%
Other values (95) 95
90.5%
ValueCountFrequency (%)
2.241510342 1
1.0%
2.589894737 1
1.0%
3.01595 1
1.0%
3.143593833 1
1.0%
3.49825 1
1.0%
3.835526316 1
1.0%
4.170453361 1
1.0%
4.183477633 1
1.0%
4.425684211 1
1.0%
4.71395 1
1.0%
ValueCountFrequency (%)
19.72594059 1
1.0%
18.75663158 1
1.0%
18.63821053 1
1.0%
18.53015789 1
1.0%
18.05736842 1
1.0%
17.96552632 1
1.0%
16.98526316 1
1.0%
16.34305263 1
1.0%
16.18831579 1
1.0%
16.1717 1
1.0%

YW-YA
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct105
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.4413716
Minimum1.3088438
Maximum16.476518
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size968.0 B
2023-02-26T07:18:32.198315image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1.3088438
5-th percentile2.4927323
Q15.3012333
median8.3591617
Q311.385589
95-th percentile15.220178
Maximum16.476518
Range15.167675
Interquartile range (IQR)6.0843556

Descriptive statistics

Standard deviation3.826294
Coefficient of variation (CV)0.4532787
Kurtosis-0.86935541
Mean8.4413716
Median Absolute Deviation (MAD)3.0579284
Skewness0.14798644
Sum886.34402
Variance14.640526
MonotonicityNot monotonic
2023-02-26T07:18:32.657087image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.922509325 1
 
1.0%
7.403397661 1
 
1.0%
6.483394872 1
 
1.0%
10.80574818 1
 
1.0%
3.480716665 1
 
1.0%
2.182339074 1
 
1.0%
13.97025 1
 
1.0%
11.20365552 1
 
1.0%
4.658194448 1
 
1.0%
3.339541162 1
 
1.0%
Other values (95) 95
90.5%
ValueCountFrequency (%)
1.30884376 1
1.0%
1.741510342 1
1.0%
2.144848654 1
1.0%
2.182339074 1
1.0%
2.376103492 1
1.0%
2.41395 1
1.0%
2.807861528 1
1.0%
2.841401046 1
1.0%
2.857845708 1
1.0%
3.153477633 1
1.0%
ValueCountFrequency (%)
16.47651846 1
1.0%
15.85643158 1
1.0%
15.83753194 1
1.0%
15.66305789 1
1.0%
15.35410463 1
1.0%
15.30701842 1
1.0%
14.87281789 1
1.0%
14.2702193 1
1.0%
13.97025 1
1.0%
13.79996316 1
1.0%

YP
Real number (ℝ)

Distinct104
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.552915
Minimum6.22
Maximum21.415789
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size968.0 B
2023-02-26T07:18:32.904427image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum6.22
5-th percentile9.94
Q112.777778
median14.884211
Q316.778947
95-th percentile19.452053
Maximum21.415789
Range15.195789
Interquartile range (IQR)4.0011696

Descriptive statistics

Standard deviation3.1149657
Coefficient of variation (CV)0.21404411
Kurtosis-0.22273236
Mean14.552915
Median Absolute Deviation (MAD)2.0210526
Skewness-0.28960099
Sum1528.0561
Variance9.7030113
MonotonicityNot monotonic
2023-02-26T07:18:33.142789image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.88421053 2
 
1.9%
10.65789474 1
 
1.0%
14.76842105 1
 
1.0%
17.395 1
 
1.0%
13.09 1
 
1.0%
6.22 1
 
1.0%
8.395 1
 
1.0%
16.345 1
 
1.0%
13.24 1
 
1.0%
20.51578947 1
 
1.0%
Other values (94) 94
89.5%
ValueCountFrequency (%)
6.22 1
1.0%
6.81 1
1.0%
8.384210526 1
1.0%
8.395 1
1.0%
9.215789474 1
1.0%
9.911111111 1
1.0%
10.05555556 1
1.0%
10.05789474 1
1.0%
10.07777778 1
1.0%
10.2 1
1.0%
ValueCountFrequency (%)
21.41578947 1
1.0%
20.51578947 1
1.0%
20.31 1
1.0%
19.65 1
1.0%
19.56842105 1
1.0%
19.52631579 1
1.0%
19.155 1
1.0%
18.81052632 1
1.0%
18.74210526 1
1.0%
18.7 1
1.0%

YP-YA
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct105
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.641583
Minimum4.8677667
Maximum19.093984
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size968.0 B
2023-02-26T07:18:33.396112image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum4.8677667
5-th percentile8.2904084
Q110.396532
median12.944874
Q314.489
95-th percentile17.0076
Maximum19.093984
Range14.226217
Interquartile range (IQR)4.0924684

Descriptive statistics

Standard deviation2.9108437
Coefficient of variation (CV)0.23025942
Kurtosis-0.22043611
Mean12.641583
Median Absolute Deviation (MAD)1.710126
Skewness-0.29776834
Sum1327.3662
Variance8.4730109
MonotonicityNot monotonic
2023-02-26T07:18:33.632518image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.776599997 1
 
1.0%
12.80260819 1
 
1.0%
16.40614487 1
 
1.0%
11.79034414 1
 
1.0%
4.867766665 1
 
1.0%
7.561389074 1
 
1.0%
14.489 1
 
1.0%
11.42480552 1
 
1.0%
19.09398392 1
 
1.0%
9.942772011 1
 
1.0%
Other values (95) 95
90.5%
ValueCountFrequency (%)
4.867766665 1
1.0%
5.687853492 1
1.0%
6.058227193 1
1.0%
6.704302632 1
1.0%
7.561389074 1
1.0%
8.217044295 1
1.0%
8.583864656 1
1.0%
8.616464622 1
1.0%
8.776599997 1
1.0%
8.785222222 1
1.0%
ValueCountFrequency (%)
19.09398392 1
1.0%
18.19941758 1
1.0%
18.13622295 1
1.0%
17.33801681 1
1.0%
17.06057787 1
1.0%
17.00892053 1
1.0%
17.00231579 1
1.0%
16.70132105 1
1.0%
16.59667266 1
1.0%
16.40614487 1
1.0%

WPP
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct105
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.742421
Minimum5.8595106
Maximum30.546388
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size968.0 B
2023-02-26T07:18:33.895811image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum5.8595106
5-th percentile9.9019526
Q115.978079
median20.227692
Q324.284394
95-th percentile28.994124
Maximum30.546388
Range24.686878
Interquartile range (IQR)8.3063155

Descriptive statistics

Standard deviation5.7217809
Coefficient of variation (CV)0.28982164
Kurtosis-0.67650578
Mean19.742421
Median Absolute Deviation (MAD)4.1638601
Skewness-0.1655214
Sum2072.9542
Variance32.738777
MonotonicityNot monotonic
2023-02-26T07:18:34.154086image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22.78542156 1
 
1.0%
21.82064629 1
 
1.0%
13.94330689 1
 
1.0%
24.39155175 1
 
1.0%
14.81184713 1
 
1.0%
9.174859462 1
 
1.0%
29.19143502 1
 
1.0%
27.69855904 1
 
1.0%
12.82520944 1
 
1.0%
11.70273262 1
 
1.0%
Other values (95) 95
90.5%
ValueCountFrequency (%)
5.859510627 1
1.0%
8.197898459 1
1.0%
9.174859462 1
1.0%
9.526964179 1
1.0%
9.628949989 1
1.0%
9.856437995 1
1.0%
10.08401086 1
1.0%
10.51233126 1
1.0%
11.02230527 1
1.0%
11.70273262 1
1.0%
ValueCountFrequency (%)
30.54638825 1
1.0%
30.04386982 1
1.0%
29.7558738 1
1.0%
29.52194191 1
1.0%
29.27571476 1
1.0%
29.19143502 1
1.0%
28.2048787 1
1.0%
27.8135893 1
1.0%
27.74486022 1
1.0%
27.69855904 1
1.0%

WPA
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct105
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7070825
Minimum1.3070452
Maximum6.9823857
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size968.0 B
2023-02-26T07:18:34.411398image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1.3070452
5-th percentile1.7967808
Q12.981625
median3.7912417
Q34.2899837
95-th percentile5.7644609
Maximum6.9823857
Range5.6753405
Interquartile range (IQR)1.3083587

Descriptive statistics

Standard deviation1.1210914
Coefficient of variation (CV)0.30241879
Kurtosis0.54775682
Mean3.7070825
Median Absolute Deviation (MAD)0.70532864
Skewness0.28487944
Sum389.24366
Variance1.2568459
MonotonicityNot monotonic
2023-02-26T07:18:34.633842image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.869042224 1
 
1.0%
4.490710869 1
 
1.0%
1.845215366 1
 
1.0%
2.618716658 1
 
1.0%
4.144274913 1
 
1.0%
2.535938291 1
 
1.0%
3.423382254 1
 
1.0%
3.861959506 1
 
1.0%
2.999170064 1
 
1.0%
2.331627393 1
 
1.0%
Other values (95) 95
90.5%
ValueCountFrequency (%)
1.307045191 1
1.0%
1.37694559 1
1.0%
1.451486777 1
1.0%
1.590149384 1
1.0%
1.595350013 1
1.0%
1.784672186 1
1.0%
1.845215366 1
1.0%
2.167004194 1
1.0%
2.233937904 1
1.0%
2.331627393 1
1.0%
ValueCountFrequency (%)
6.982385669 1
1.0%
6.897061796 1
1.0%
6.176932657 1
1.0%
6.022856894 1
1.0%
5.954452789 1
1.0%
5.872405432 1
1.0%
5.332682944 1
1.0%
5.260110239 1
1.0%
5.243172691 1
1.0%
5.049382178 1
1.0%

CROPPING_INTENSITY
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size968.0 B
1.0
86 
1.95
13 
1.9523809523809523
 
3
1.2085365853658536
 
2
1.9
 
1

Length

Max length18
Median length3
Mean length3.8380952
Min length3

Characters and Unicode

Total characters403
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 86
81.9%
1.95 13
 
12.4%
1.9523809523809523 3
 
2.9%
1.2085365853658536 2
 
1.9%
1.9 1
 
1.0%

Length

2023-02-26T07:18:34.972894image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-26T07:18:35.148465image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 86
81.9%
1.95 13
 
12.4%
1.9523809523809523 3
 
2.9%
1.2085365853658536 2
 
1.9%
1.9 1
 
1.0%

Most occurring characters

ValueCountFrequency (%)
1 105
26.1%
. 105
26.1%
0 94
23.3%
5 32
 
7.9%
9 23
 
5.7%
3 15
 
3.7%
8 12
 
3.0%
2 11
 
2.7%
6 6
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 298
73.9%
Other Punctuation 105
 
26.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 105
35.2%
0 94
31.5%
5 32
 
10.7%
9 23
 
7.7%
3 15
 
5.0%
8 12
 
4.0%
2 11
 
3.7%
6 6
 
2.0%
Other Punctuation
ValueCountFrequency (%)
. 105
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 403
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 105
26.1%
. 105
26.1%
0 94
23.3%
5 32
 
7.9%
9 23
 
5.7%
3 15
 
3.7%
8 12
 
3.0%
2 11
 
2.7%
6 6
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 403
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 105
26.1%
. 105
26.1%
0 94
23.3%
5 32
 
7.9%
9 23
 
5.7%
3 15
 
3.7%
8 12
 
3.0%
2 11
 
2.7%
6 6
 
1.5%

YW_CV_TEMPORAL
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct105
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.34812497
Minimum0.04890852
Maximum1.033194
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size968.0 B
2023-02-26T07:18:35.338954image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.04890852
5-th percentile0.074761454
Q10.14593774
median0.29859303
Q30.46499402
95-th percentile0.78222971
Maximum1.033194
Range0.9842855
Interquartile range (IQR)0.31905629

Descriptive statistics

Standard deviation0.23632249
Coefficient of variation (CV)0.67884383
Kurtosis-0.13259723
Mean0.34812497
Median Absolute Deviation (MAD)0.1652349
Skewness0.80518147
Sum36.553122
Variance0.055848319
MonotonicityNot monotonic
2023-02-26T07:18:35.528448image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1943820833 1
 
1.0%
0.4386766854 1
 
1.0%
0.7874104085 1
 
1.0%
0.1299421625 1
 
1.0%
0.2344424107 1
 
1.0%
0.6804385167 1
 
1.0%
0.1068132357 1
 
1.0%
0.1034900627 1
 
1.0%
0.6209488589 1
 
1.0%
0.4582588825 1
 
1.0%
Other values (95) 95
90.5%
ValueCountFrequency (%)
0.0489085198 1
1.0%
0.05619500496 1
1.0%
0.06415440207 1
1.0%
0.06452744318 1
1.0%
0.07324288824 1
1.0%
0.07369123516 1
1.0%
0.07904233084 1
1.0%
0.08054678829 1
1.0%
0.08310024337 1
1.0%
0.08518535519 1
1.0%
ValueCountFrequency (%)
1.033194019 1
1.0%
0.9799476092 1
1.0%
0.8545824557 1
1.0%
0.8087758762 1
1.0%
0.7874104085 1
1.0%
0.7864376439 1
1.0%
0.7653979879 1
1.0%
0.7468358203 1
1.0%
0.743862225 1
1.0%
0.730004073 1
1.0%

YP_CV_TEMPORAL
Real number (ℝ)

Distinct105
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.059405003
Minimum0.021602559
Maximum0.10641946
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size968.0 B
2023-02-26T07:18:35.723885image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.021602559
5-th percentile0.034449085
Q10.048176765
median0.058480589
Q30.070274791
95-th percentile0.089178372
Maximum0.10641946
Range0.084816898
Interquartile range (IQR)0.022098026

Descriptive statistics

Standard deviation0.016593499
Coefficient of variation (CV)0.27932832
Kurtosis-0.13110238
Mean0.059405003
Median Absolute Deviation (MAD)0.010527324
Skewness0.33724101
Sum6.2375253
Variance0.00027534422
MonotonicityNot monotonic
2023-02-26T07:18:35.912418image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.03270007633 1
 
1.0%
0.05240245438 1
 
1.0%
0.08416922062 1
 
1.0%
0.0702747914 1
 
1.0%
0.06228845739 1
 
1.0%
0.06308785075 1
 
1.0%
0.05929953696 1
 
1.0%
0.06316333087 1
 
1.0%
0.0468727945 1
 
1.0%
0.05366409542 1
 
1.0%
Other values (95) 95
90.5%
ValueCountFrequency (%)
0.02160255896 1
1.0%
0.02885206795 1
1.0%
0.03261726383 1
1.0%
0.03270007633 1
1.0%
0.03397467578 1
1.0%
0.03422614969 1
1.0%
0.03534082733 1
1.0%
0.03591019748 1
1.0%
0.03614518429 1
1.0%
0.03681706753 1
1.0%
ValueCountFrequency (%)
0.1064194572 1
1.0%
0.09495956858 1
1.0%
0.09379208855 1
1.0%
0.09317798846 1
1.0%
0.09120551048 1
1.0%
0.08992902041 1
1.0%
0.0861757778 1
1.0%
0.08520391792 1
1.0%
0.08509401645 1
1.0%
0.08496879157 1
1.0%

YA_CV_TEMPORAL
Real number (ℝ)

Distinct77
Distinct (%)98.7%
Missing27
Missing (%)25.7%
Infinite0
Infinite (%)0.0%
Mean0.24531908
Minimum0.028745604
Maximum0.70427744
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size968.0 B
2023-02-26T07:18:36.108873image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.028745604
5-th percentile0.081594279
Q10.15783792
median0.22896046
Q30.30234985
95-th percentile0.48326748
Maximum0.70427744
Range0.67553183
Interquartile range (IQR)0.14451193

Descriptive statistics

Standard deviation0.12710931
Coefficient of variation (CV)0.51813872
Kurtosis1.3245728
Mean0.24531908
Median Absolute Deviation (MAD)0.072946813
Skewness1.0140571
Sum19.134888
Variance0.016156777
MonotonicityNot monotonic
2023-02-26T07:18:36.445954image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1823056152 2
 
1.9%
0.0287456038 1
 
1.0%
0.08806005556 1
 
1.0%
0.2695038234 1
 
1.0%
0.1113419697 1
 
1.0%
0.1041734522 1
 
1.0%
0.08251895614 1
 
1.0%
0.1133710395 1
 
1.0%
0.1819430573 1
 
1.0%
0.1272002677 1
 
1.0%
Other values (67) 67
63.8%
(Missing) 27
25.7%
ValueCountFrequency (%)
0.0287456038 1
1.0%
0.05049697417 1
1.0%
0.06055536634 1
1.0%
0.07635443994 1
1.0%
0.08251895614 1
1.0%
0.08806005556 1
1.0%
0.1036154498 1
1.0%
0.1041734522 1
1.0%
0.104935617 1
1.0%
0.1113419697 1
1.0%
ValueCountFrequency (%)
0.7042774356 1
1.0%
0.5298235606 1
1.0%
0.4890777001 1
1.0%
0.4840351562 1
1.0%
0.4831320061 1
1.0%
0.4782465562 1
1.0%
0.4713657206 1
1.0%
0.4709190918 1
1.0%
0.4291479087 1
1.0%
0.4266080397 1
1.0%

CLIMATEZONE
Real number (ℝ)

Distinct35
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8140.0476
Minimum5401
Maximum10901
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size968.0 B
2023-02-26T07:18:36.638477image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum5401
5-th percentile5901
Q17201
median7501
Q39701
95-th percentile10381
Maximum10901
Range5500
Interquartile range (IQR)2500

Descriptive statistics

Standard deviation1497.9791
Coefficient of variation (CV)0.18402584
Kurtosis-1.2129323
Mean8140.0476
Median Absolute Deviation (MAD)1000
Skewness0.26751407
Sum854705
Variance2243941.4
MonotonicityNot monotonic
2023-02-26T07:18:36.798014image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
7201 8
 
7.6%
7301 8
 
7.6%
7401 8
 
7.6%
7501 8
 
7.6%
10101 7
 
6.7%
7601 6
 
5.7%
10301 5
 
4.8%
10201 5
 
4.8%
6501 5
 
4.8%
6401 3
 
2.9%
Other values (25) 42
40.0%
ValueCountFrequency (%)
5401 1
 
1.0%
5501 2
 
1.9%
5701 1
 
1.0%
5801 2
 
1.9%
6301 1
 
1.0%
6401 3
2.9%
6501 5
4.8%
6601 2
 
1.9%
6701 1
 
1.0%
6801 3
2.9%
ValueCountFrequency (%)
10901 1
 
1.0%
10501 2
 
1.9%
10401 3
2.9%
10301 5
4.8%
10201 5
4.8%
10101 7
6.7%
9901 2
 
1.9%
9801 1
 
1.0%
9701 2
 
1.9%
9601 1
 
1.0%

AREA_IN_CLIMATEZONE_HA
Real number (ℝ)

Distinct105
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57672.076
Minimum424
Maximum196505
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size968.0 B
2023-02-26T07:18:37.748509image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum424
5-th percentile2621.6
Q114203
median45676
Q384227
95-th percentile179830.4
Maximum196505
Range196081
Interquartile range (IQR)70024

Descriptive statistics

Standard deviation52992.017
Coefficient of variation (CV)0.91885051
Kurtosis0.58257515
Mean57672.076
Median Absolute Deviation (MAD)34013
Skewness1.1431932
Sum6055568
Variance2.8081538 × 109
MonotonicityNot monotonic
2023-02-26T07:18:38.207242image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
158555 1
 
1.0%
22096 1
 
1.0%
196505 1
 
1.0%
53113 1
 
1.0%
123436 1
 
1.0%
182002 1
 
1.0%
62700 1
 
1.0%
57024 1
 
1.0%
96498 1
 
1.0%
107804 1
 
1.0%
Other values (95) 95
90.5%
ValueCountFrequency (%)
424 1
1.0%
1226 1
1.0%
1457 1
1.0%
2197 1
1.0%
2349 1
1.0%
2431 1
1.0%
3384 1
1.0%
3571 1
1.0%
3602 1
1.0%
3755 1
1.0%
ValueCountFrequency (%)
196505 1
1.0%
195851 1
1.0%
194868 1
1.0%
192995 1
1.0%
186598 1
1.0%
182002 1
1.0%
171144 1
1.0%
166208 1
1.0%
161472 1
1.0%
158555 1
1.0%

MIN_N_INPUT_TARGET_30_PERC
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct105
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.743655
Minimum13.584911
Maximum119.55116
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size968.0 B
2023-02-26T07:18:38.562293image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum13.584911
5-th percentile23.651586
Q141.784073
median61.77686
Q379.223285
95-th percentile107.69378
Maximum119.55116
Range105.96624
Interquartile range (IQR)37.439212

Descriptive statistics

Standard deviation25.755933
Coefficient of variation (CV)0.41049463
Kurtosis-0.79725013
Mean62.743655
Median Absolute Deviation (MAD)19.425054
Skewness0.14056561
Sum6588.0838
Variance663.3681
MonotonicityNot monotonic
2023-02-26T07:18:38.776759image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53.35638827 1
 
1.0%
56.49601276 1
 
1.0%
45.28636364 1
 
1.0%
73.36608509 1
 
1.0%
29.29060606 1
 
1.0%
18.27848485 1
 
1.0%
95.91666667 1
 
1.0%
78.90212121 1
 
1.0%
36.84848485 1
 
1.0%
25.27547492 1
 
1.0%
Other values (95) 95
90.5%
ValueCountFrequency (%)
13.58491116 1
1.0%
15.69633174 1
1.0%
18.27848485 1
1.0%
19.05208384 1
1.0%
21.20151515 1
1.0%
23.24561404 1
1.0%
25.27547492 1
1.0%
25.3544099 1
1.0%
26.82232855 1
1.0%
28.56939394 1
1.0%
ValueCountFrequency (%)
119.5511551 1
1.0%
113.676555 1
1.0%
112.9588517 1
1.0%
112.3039872 1
1.0%
109.4385965 1
1.0%
108.8819777 1
1.0%
102.9409888 1
1.0%
99.04880383 1
1.0%
98.11100478 1
1.0%
98.01030303 1
1.0%

MIN_N_INPUT_TARGET_50_PERC
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct105
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104.57276
Minimum22.641519
Maximum199.25193
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size968.0 B
2023-02-26T07:18:39.030044image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum22.641519
5-th percentile39.41931
Q169.640122
median102.96143
Q3132.03881
95-th percentile179.48963
Maximum199.25193
Range176.61041
Interquartile range (IQR)62.398687

Descriptive statistics

Standard deviation42.926555
Coefficient of variation (CV)0.41049463
Kurtosis-0.79725013
Mean104.57276
Median Absolute Deviation (MAD)32.375091
Skewness0.14056561
Sum10980.14
Variance1842.6892
MonotonicityNot monotonic
2023-02-26T07:18:39.299321image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88.92731378 1
 
1.0%
94.16002127 1
 
1.0%
75.47727273 1
 
1.0%
122.2768085 1
 
1.0%
48.81767677 1
 
1.0%
30.46414141 1
 
1.0%
159.8611111 1
 
1.0%
131.5035354 1
 
1.0%
61.41414141 1
 
1.0%
42.12579153 1
 
1.0%
Other values (95) 95
90.5%
ValueCountFrequency (%)
22.6415186 1
1.0%
26.1605529 1
1.0%
30.46414141 1
1.0%
31.75347306 1
1.0%
35.33585859 1
1.0%
38.74269006 1
1.0%
42.12579153 1
1.0%
42.25734983 1
1.0%
44.70388091 1
1.0%
47.61565657 1
1.0%
ValueCountFrequency (%)
199.2519252 1
1.0%
189.460925 1
1.0%
188.2647528 1
1.0%
187.1733121 1
1.0%
182.3976608 1
1.0%
181.4699628 1
1.0%
171.5683147 1
1.0%
165.0813397 1
1.0%
163.5183413 1
1.0%
163.3505051 1
1.0%

MIN_N_INPUT_TARGET_80_PERC
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct105
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean175.24525
Minimum37.943138
Maximum333.91061
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size968.0 B
2023-02-26T07:18:39.576617image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum37.943138
5-th percentile66.059717
Q1116.7044
median172.54496
Q3221.27344
95-th percentile300.79254
Maximum333.91061
Range295.96747
Interquartile range (IQR)104.56905

Descriptive statistics

Standard deviation71.937233
Coefficient of variation (CV)0.41049463
Kurtosis-0.79725013
Mean175.24525
Median Absolute Deviation (MAD)54.254864
Skewness0.14056561
Sum18400.751
Variance5174.9655
MonotonicityNot monotonic
2023-02-26T07:18:40.029372image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
149.0262811 1
 
1.0%
157.7953635 1
 
1.0%
126.4864167 1
 
1.0%
204.9140727 1
 
1.0%
81.80969955 1
 
1.0%
51.0524552 1
 
1.0%
267.8986452 1
 
1.0%
220.3764175 1
 
1.0%
102.9191225 1
 
1.0%
70.59529613 1
 
1.0%
Other values (95) 95
90.5%
ValueCountFrequency (%)
37.94313775 1
1.0%
43.84041016 1
1.0%
51.0524552 1
1.0%
53.21314456 1
1.0%
59.2165823 1
1.0%
64.92582284 1
1.0%
70.59529613 1
1.0%
70.81576434 1
1.0%
74.91571309 1
1.0%
79.79532856 1
1.0%
ValueCountFrequency (%)
333.9106081 1
1.0%
317.5026421 1
1.0%
315.4980712 1
1.0%
313.6690117 1
1.0%
305.6658739 1
1.0%
304.1112178 1
1.0%
287.5178257 1
1.0%
276.6468152 1
1.0%
274.0275093 1
1.0%
273.7462457 1
1.0%

Interactions

2023-02-26T07:18:23.538514image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:16:56.688780image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:02.878227image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:07.497869image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:10.634517image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:13.568630image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:17.293665image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:20.342513image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:23.906977image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:27.750697image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:34.658222image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:38.482030image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:44.306414image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:49.558369image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:55.948275image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:18:03.182925image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:18:11.916568image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:18:17.680150image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:18:23.705070image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:16:56.983989image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:03.240262image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:07.676391image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:10.824967image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:13.725212image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:17.444263image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:20.580875image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:24.067547image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:27.941189image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:34.841729image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:38.680463image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:44.547768image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:50.143806image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:56.491824image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:18:03.575874image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:18:12.150937image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:18:18.007272image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:18:23.932425image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:16:57.447750image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:03.516520image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:07.844941image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:10.980554image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:13.882789image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:17.592888image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:20.745472image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:24.245075image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:28.396969image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:35.042193image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:38.942764image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:44.817048image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:50.663412image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:57.150062image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:18:04.375735image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:18:12.457120image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:18:18.288523image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:18:24.139872image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:16:57.772888image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:03.959332image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:08.031443image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:11.154088image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:14.065302image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:17.748450image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:20.939914image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:24.426587image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:28.727086image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:35.295516image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:39.216032image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:45.055413image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:51.080297image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:57.476188image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:18:04.983110image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:18:12.847074image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:18:18.774224image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:18:24.330361image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:16:58.076070image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:04.318372image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:08.221933image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2023-02-26T07:17:16.797992image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:19.825937image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:23.322540image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:26.976767image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:33.016612image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:38.015241image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:43.476635image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:48.513164image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:55.060649image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:18:01.863455image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:18:10.881337image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:18:16.733681image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:18:22.848326image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:18:26.435730image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:02.033484image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:07.147804image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:10.251502image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:13.272422image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:16.962554image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:19.983472image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:23.539959image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:27.159278image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:33.297859image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:38.191769image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:43.829690image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:48.922070image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:55.331924image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:18:02.104810image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:18:11.156597image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:18:17.045845image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:18:23.112618image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:18:26.599293image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:02.505224image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:07.316353image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:10.436009image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:13.429003image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:17.130105image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:20.194906image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:23.721473image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:27.508345image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:34.296188image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:38.343365image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:44.067054image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:49.245205image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:17:55.614170image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:18:02.697224image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:18:11.566500image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:18:17.381947image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-26T07:18:23.358958image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-02-26T07:18:40.343529image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
LONGITUDELATITUDEELEVATION_METERYAYWYW-YAYPYP-YAWPPWPAYW_CV_TEMPORALYP_CV_TEMPORALYA_CV_TEMPORALCLIMATEZONEAREA_IN_CLIMATEZONE_HAMIN_N_INPUT_TARGET_30_PERCMIN_N_INPUT_TARGET_50_PERCMIN_N_INPUT_TARGET_80_PERCCOUNTRYCROPPING_INTENSITY
LONGITUDE1.000-0.2500.8230.3970.2080.1470.2950.219-0.1330.144-0.2450.0700.337-0.823-0.3780.2080.2080.2080.6000.276
LATITUDE-0.2501.000-0.2480.080-0.039-0.048-0.347-0.3660.0480.156-0.069-0.036-0.0400.4300.045-0.039-0.039-0.0390.6210.413
ELEVATION_METER0.823-0.2481.0000.5150.2790.2040.2680.144-0.0680.281-0.3760.1530.346-0.824-0.3330.2790.2790.2790.4090.259
YA0.3970.0800.5151.0000.6200.4960.3620.1470.4390.829-0.5510.104-0.057-0.363-0.1600.6200.6200.6200.2990.000
YW0.208-0.0390.2790.6201.0000.9860.5970.4930.8820.261-0.7710.188-0.152-0.162-0.1021.0001.0001.0000.2370.000
YW-YA0.147-0.0480.2040.4960.9861.0000.5980.5270.8960.132-0.7480.184-0.159-0.100-0.0770.9860.9860.9860.2430.000
YP0.295-0.3470.2680.3620.5970.5981.0000.9650.3690.003-0.1330.1030.075-0.291-0.0910.5970.5970.5970.2740.000
YP-YA0.219-0.3660.1440.1470.4930.5270.9651.0000.303-0.195-0.0220.0730.076-0.215-0.0810.4930.4930.4930.3490.000
WPP-0.1330.048-0.0680.4390.8820.8960.3690.3031.0000.261-0.7210.119-0.3600.1680.0370.8820.8820.8820.1800.134
WPA0.1440.1560.2810.8290.2610.1320.003-0.1950.2611.000-0.3390.058-0.127-0.151-0.0550.2610.2610.2610.1370.000
YW_CV_TEMPORAL-0.245-0.069-0.376-0.551-0.771-0.748-0.133-0.022-0.721-0.3391.000-0.1020.1450.1810.146-0.771-0.771-0.7710.1970.231
YP_CV_TEMPORAL0.070-0.0360.1530.1040.1880.1840.1030.0730.1190.058-0.1021.0000.225-0.184-0.1050.1880.1880.1880.1800.410
YA_CV_TEMPORAL0.337-0.0400.346-0.057-0.152-0.1590.0750.076-0.360-0.1270.1450.2251.000-0.412-0.279-0.152-0.152-0.1520.3390.036
CLIMATEZONE-0.8230.430-0.824-0.363-0.162-0.100-0.291-0.2150.168-0.1510.181-0.184-0.4121.0000.327-0.162-0.162-0.1620.4440.248
AREA_IN_CLIMATEZONE_HA-0.3780.045-0.333-0.160-0.102-0.077-0.091-0.0810.037-0.0550.146-0.105-0.2790.3271.000-0.102-0.102-0.1020.2070.144
MIN_N_INPUT_TARGET_30_PERC0.208-0.0390.2790.6201.0000.9860.5970.4930.8820.261-0.7710.188-0.152-0.162-0.1021.0001.0001.0000.2370.000
MIN_N_INPUT_TARGET_50_PERC0.208-0.0390.2790.6201.0000.9860.5970.4930.8820.261-0.7710.188-0.152-0.162-0.1021.0001.0001.0000.2370.000
MIN_N_INPUT_TARGET_80_PERC0.208-0.0390.2790.6201.0000.9860.5970.4930.8820.261-0.7710.188-0.152-0.162-0.1021.0001.0001.0000.2370.000
COUNTRY0.6000.6210.4090.2990.2370.2430.2740.3490.1800.1370.1970.1800.3390.4440.2070.2370.2370.2371.0000.507
CROPPING_INTENSITY0.2760.4130.2590.0000.0000.0000.0000.0000.1340.0000.2310.4100.0360.2480.1440.0000.0000.0000.5071.000

Missing values

2023-02-26T07:18:27.172757image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-26T07:18:28.183091image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-02-26T07:18:28.557054image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

STATIONNAMELONGITUDELATITUDEELEVATION_METERCOUNTRYCROPYAYWYW-YAYPYP-YAWPPWPACROPPING_INTENSITYYW_CV_TEMPORALYP_CV_TEMPORALYA_CV_TEMPORALCLIMATEZONEAREA_IN_CLIMATEZONE_HAMIN_N_INPUT_TARGET_30_PERCMIN_N_INPUT_TARGET_50_PERCMIN_N_INPUT_TARGET_80_PERC
0Bobo-Dioulasso-4.31711.167445.0Burkina FasoRainfed maize1.8812958.8038046.92250910.6578958.77660022.7854224.8690421.00.1943820.0327000.1628931030115855553.35638888.927314149.026281
1Bogandé-0.13712.974281.0Burkina FasoRainfed maize1.0023785.2760384.27366010.0555569.05317714.3702582.7301611.00.6461200.0481450.175539101013284631.97598953.29331589.310069
2Boromo-2.93311.750243.0Burkina FasoRainfed maize1.4613138.3801986.91888510.0777788.61646522.4851023.9208831.00.2402370.0578150.1996341020116147250.78907984.648465141.855695
3Dédougou-3.48312.467299.0Burkina FasoRainfed maize1.4740305.8458954.37186510.0578958.58386515.6074683.9353901.00.5713440.0481770.201487102015879135.42966559.04944298.956309
4Fada Ngourma0.36712.033294.0Burkina FasoRainfed maize1.3448196.6259265.28110710.4166679.07184818.4509853.7448701.00.4113720.0476320.153830102014828940.15712766.928545112.160277
5Gaoua-3.18310.333339.0Burkina FasoRainfed maize1.1017397.2391586.13741910.2000009.09826120.0788163.0558271.00.3841220.0522880.177231103016932943.87368473.122807122.540753
6Ouahigouya-2.41713.567315.0Burkina FasoRainfed maize0.9205666.3286675.4081019.9111118.99054517.2596022.5105771.00.4638280.0527000.258175101011847438.35555663.925926107.128424
7Adet37.48011.2702240.0EthiopiaRainfed maize3.18530016.98526313.79996317.49473714.30943721.7962634.0875221.00.0928150.0506790.249962670193969102.940989171.568315287.517826
8Ambo37.8358.9602100.0EthiopiaRainfed maize2.96680016.18831613.22151617.34736814.38056821.0688863.8612521.00.1243800.0849690.22514655013198998.111005163.518341274.027509
9Arbaminch37.4006.0501280.0EthiopiaRainfed maize2.1915269.2526327.06110617.98947415.79794815.9043143.7670051.00.4892530.0472370.4266085801375556.07655593.460925156.623803
STATIONNAMELONGITUDELATITUDEELEVATION_METERCOUNTRYCROPYAYWYW-YAYPYP-YAWPPWPACROPPING_INTENSITYYW_CV_TEMPORALYP_CV_TEMPORALYA_CV_TEMPORALCLIMATEZONEAREA_IN_CLIMATEZONE_HAMIN_N_INPUT_TARGET_30_PERCMIN_N_INPUT_TARGET_50_PERCMIN_N_INPUT_TARGET_80_PERC
95Choma26.988000-16.8116671278.0ZambiaRainfed maize2.3878189.2797376.89191813.37894710.99112919.6233255.0493821.00.4201640.065188NaN73014921856.24082993.734716157.082627
96Kabwe28.451111-14.4438891207.0ZambiaRainfed maize3.98451013.3983169.41380617.20000013.21549023.1920646.8970621.00.2678020.063304NaN73018797681.201914135.336523226.799820
97Kasama31.138889-10.2213891384.0ZambiaRainfed maize2.80067918.63821115.83753218.64736815.84669026.1451073.9287061.00.0645270.065126NaN760112894112.958852188.264753315.498071
98Livingstone25.865833-17.735000986.0ZambiaRainfed maize0.9068696.6504435.74357417.91578917.00892113.0877351.7846721.00.8545820.069448NaN72014084340.30571567.176191112.575288
99Mansa28.850000-11.1000001384.0ZambiaRainfed maize2.61142217.96552615.35410518.15263215.54121025.9847533.7770751.00.0736910.076847NaN75017422108.881978181.469963304.111218
100Mongu23.162300-15.2545001053.0ZambiaRainfed maize0.79385611.31184210.51798617.08421116.29035419.6204151.3769461.00.3659170.074788NaN73011420368.556619114.261031191.481063
101Mpika31.454444-11.8380561402.0ZambiaRainfed maize3.24942215.93742112.68799916.80000013.55057826.1551785.3326831.00.1293180.067781NaN7501940296.590431160.984051269.780492
102Mumbwa27.183300-15.0667001218.0ZambiaRainfed maize2.55939311.5345268.97513316.50000013.94060721.2274824.7101611.00.4574760.070649NaN73015244769.906220116.510367195.250547
103zam_rfmz132.908816-10.657473717.0ZambiaRainfed maize1.41000014.72920013.31920016.06500014.65500027.6806892.6498231.00.1459380.075546NaN7301219789.267879148.779798249.328345
104zam_rfmz232.100146-11.5691211081.0ZambiaRainfed maize3.24942219.72594116.47651820.31000017.06057829.5219424.8631021.00.0926630.057750NaN74012431119.551155199.251925333.910608